PSO BASED TAKAGI-SUGENO FUZZY PID CONTROLLER DESIGN FOR SPEED CONTROL OF PERMANENT MAGNET SYNCHRONOUS MOTOR

A permanent magnet synchronous motor (PMSM) is one kind of popular motor. They are utilized in industrial applications because their abilities included operation at a constant speed, no need for an excitation current, no rotor losses, and small size. In the following paper, a fuzzy evolutionary algorithm is combined with a proportional-integral-derivative (PID) controller to control the speed of a PMSM. In this structure, to overcome the PMSM challenges, including nonlinear nature, cross-coupling, air gap flux, and cogging torque in operation, a Takagi-Sugeno fuzzy logic-PID (TSFL-PID) controller is designed. Additionally, the particle swarm optimization (PSO) algorithm is developed to optimize the membership functions' parameters and rule bases of the fuzzy logic PID controller. For evaluating the proposed controller's performance, the genetic algorithm (GA), as another evolutionary algorithm, is incorporated into the fuzzy PID controller. The results of the speed control of PMSM are compared. The obtained results demonstrate that although both controllers have excellent performance; however, the PSO based TSFL-PID controller indicates more superiority

Adaptive Integral Terminal Sliding Mode Control for the Nonlinear Active Vehicle Suspension System under External Disturbances and Uncertainties

Suspension system is one of the most effective vehicle components that play an essential role in the stability and comfort of the vehicle. The passive suspension can not fully meet a car's stability and comfort requirements. Instead, an active suspension system has been proposed to improve these challenges. Active suspension minimizes the vibrations entering the body using a closed-loop control system. To this end, in this research, an integral terminal sliding mode control (integral TSMC) for an active nonlinear car suspension system under external disturbances and uncertainties is designed. First, the integral TSMC is designed to deal with the uncertainties and the external disturbances in the system when the upper bound is known. Next, an adaptation law is recommended to estimate the upper bound of uncertainties and external disturbances. The results show that the proposed integral TSMC improves the convergence rate and tracking error of the closed-loop system. The stability of the nonlinear control system is investigated and proven using Lyapunov's stability theory. The numerical results indicate a good robust performance and stability for the proposed controller for the nonlinear suspension system with different road profiles in the presence of uncertainties and external disturbances. From the results, it can also be understood that important measures such as ride comfort, road holding, and mechanical structural limitations are met using the proposed approach